Parallel Searchlight classification toolbox

The Parallel Searchlight MVPA Toolbox contains Matlab functions for classification and regression designed for parallelizing the computations of searchlight classifiers across the brain. This toolbox uses operations with sparse matrices with the purpose of reducing the computational time (CPU time). It avoids the sequential calculus of searchlight classifiers across voxels.

The maximum gain of computational speed is achieved for Gaussian Naive Bayes (GNB), since it is based on the assumption of independence between voxels. Nevertheless not all the algorithms improves the CPU time when compared with the sequential approach. This strategy produces a significant gain for those classifiers that requires iterative minimization of the cost function as: SVM and Logistic Regression (LR). The implementations of SVM and Logistic Regression uses MEX compiled C code. The windows mex compiled functions are included in the toolbox. For other OS and the cpp files were also included.

Authors:This toolbox has been developed by Marlis Ontivero-Ortega, Agustin Lage-Castellanos, and Mitchell Valdés-Sosa from the Cuban Center for Neuroscience in collaboration with Giancarlo Valente and Rainer Goebel from the Cognitive Neuroscience Department at the Maastricht University.